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Uncertainty in Biology

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Cover of 'Uncertainty in Biology'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Uncertainty in the Development of Computational Models of Biological Processes
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    Chapter 2 Reverse Engineering Under Uncertainty
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    Chapter 3 Probabilistic Computational Causal Discovery for Systems Biology
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    Chapter 4 Stochastic Modeling and Simulation Methods for Biological Processes: Overview
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    Chapter 5 The Experimental Side of Parameter Estimation
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    Chapter 6 Statistical Data Analysis and Modeling
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    Chapter 7 Optimization in Biology Parameter Estimation and the Associated Optimization Problem
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    Chapter 8 Interval Methods
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    Chapter 9 Model Extension and Model Selection
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    Chapter 10 Bayesian Model Selection Methods and Their Application to Biological ODE Systems
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    Chapter 11 Sloppiness and the Geometry of Parameter Space
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    Chapter 12 Modeling and Model Simplification to Facilitate Biological Insights and Predictions
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    Chapter 13 Sensitivity Analysis by Design of Experiments
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    Chapter 14 Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantification
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    Chapter 15 In-Silico Models of Trabecular Bone: A Sensitivity Analysis Perspective
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    Chapter 16 Neuroswarm: A Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons
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    Chapter 17 Prediction Uncertainty Estimation Despite Unidentifiability: An Overview of Recent Developments
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    Chapter 18 Computational Modeling Under Uncertainty: Challenges and Opportunities
Attention for Chapter 14: Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantification
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Chapter title
Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantification
Chapter number 14
Book title
Uncertainty in Biology
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-21296-8_14
Book ISBNs
978-3-31-921295-1, 978-3-31-921296-8
Authors

Carlo R. Laing, Laing, Carlo R.

Editors

Liesbet Geris, David Gomez-Cabrero

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Student > Ph. D. Student 2 29%
Professor 1 14%
Other 1 14%
Readers by discipline Count As %
Mathematics 2 29%
Engineering 2 29%
Business, Management and Accounting 1 14%
Neuroscience 1 14%
Physics and Astronomy 1 14%
Other 0 0%